Knowledge Recovery for Novel Classes in Open-Vocabulary Object Detection

Pretrained open-vocabulary object detection (OVD) models can recognize novel classes without ground truth annotations in a target dataset. However, adapting these models with only base class supervision tends to compromise their generalization to novel classes. We find that treating unlabeled novel-class instances as background during adaptation suppresses two types of knowledge: class-agnostic foreground knowledge and class-discriminative semantic knowledge, leading to reduced detection performance for novel classes. To address this issue, we propose KR-OVD (Knowledge Recovery for Open-Vocabulary Detection), which provides two distinct recovery pathways. One pathway uses class-aware pseudo labels to directly supply the missing classification and localization supervision for novel classes. The other pathway introduces an Inter-Class Attention Path Aggregation Network (ICA-PAN) that models inter-class relationships to enable base-to-novel semantic transfer, indirectly recovering impaired novel-class knowledge. Experiments on OV-COCO and VisDrone2019-DET demonstrate that KR-OVD recovers novel-class recognition impaired during adaptation while preserving base-class performance.